Zobrazeno 1 - 10
of 143
pro vyhledávání: '"Kang, Haeyong"'
Band-selective simulation of photoelectron intensity and converging Berry phase in trilayer graphene
Publikováno v:
Appl. Sci. Converg. Technol. 33, 91 (2024)
Berry phase is one of the key elements to understand quantum-mechanical phenomena such as the Aharonov-Bohm effect and the unconventional Hall effect in graphene. The Berry phase in monolayer and bilayer graphene has been manifested by the anisotropi
Externí odkaz:
http://arxiv.org/abs/2408.07557
Reinforcement Learning (RL) agents demonstrating proficiency in a training environment exhibit vulnerability to adversarial perturbations in input observations during deployment. This underscores the importance of building a robust agent before its r
Externí odkaz:
http://arxiv.org/abs/2408.00023
Inspired by the Lottery Ticket Hypothesis (LTH), which highlights the existence of efficient subnetworks within larger, dense networks, a high-performing Winning Subnetwork (WSN) in terms of task performance under appropriate sparsity conditions is c
Externí odkaz:
http://arxiv.org/abs/2312.11973
Neural Implicit Representation (NIR) has recently gained significant attention due to its remarkable ability to encode complex and high-dimensional data into representation space and easily reconstruct it through a trainable mapping function. However
Externí odkaz:
http://arxiv.org/abs/2306.11305
Inspired by Regularized Lottery Ticket Hypothesis (RLTH), which states that competitive smooth (non-binary) subnetworks exist within a dense network in continual learning tasks, we investigate two proposed architecture-based continual learning method
Externí odkaz:
http://arxiv.org/abs/2303.14962
Publikováno v:
IEEE Journal of Selected Topics in Signal Processing ( Volume: 10, Issue: 1, February 2016)
An algorithm based on a deep probabilistic architecture referred to as a tree-structured sum-product network (t-SPN) is considered for cell classification. The t-SPN is constructed such that the unnormalized probability is represented as conditional
Externí odkaz:
http://arxiv.org/abs/2303.09065
Autor:
Kang, Haeyong, Yoo, Chang D.
Publikováno v:
Mach. Learn. Knowl. Extr. 2023, 5(1), 287-303
An unbiased scene graph generation (SGG) algorithm referred to as Skew Class-balanced Re-weighting (SCR) is proposed for considering the unbiased predicate prediction caused by the long-tailed distribution. The prior works focus mainly on alleviating
Externí odkaz:
http://arxiv.org/abs/2301.00351
Inspired by Regularized Lottery Ticket Hypothesis (RLTH), which hypothesizes that there exist smooth (non-binary) subnetworks within a dense network that achieve the competitive performance of the dense network, we propose a few-shot class incrementa
Externí odkaz:
http://arxiv.org/abs/2209.07529
Publikováno v:
2021 IEEE International Conference on Image Processing (ICIP)
A learning algorithm referred to as Maximum Margin (MM) is proposed for considering the class-imbalance data learning issue: the trained model tends to predict the majority of classes rather than the minority ones. That is, underfitting for minority
Externí odkaz:
http://arxiv.org/abs/2206.05380
This paper considers a video caption generating network referred to as Semantic Grouping Network (SGN) that attempts (1) to group video frames with discriminating word phrases of partially decoded caption and then (2) to decode those semantically ali
Externí odkaz:
http://arxiv.org/abs/2102.00831